Human seasonal coronavirus neutralization and COVID‐19 severity

Abstract The virus severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), responsible for the global coronavirus disease‐2019 (COVID‐19) pandemic, spread rapidly around the world causing high morbidity and mortality. However, there are four known, endemic seasonal coronaviruses in humans (HCoVs), and whether antibodies for these HCoVs play a role in severity of COVID‐19 disease has generated a lot of interest. Of these seasonal viruses NL63 is of particular interest as it uses the same cell entry receptor as SARS‐CoV‐2. We use functional, neutralizing assays to investigate cross‐reactive antibodies and their relationship with COVID‐19 severity. We analyzed the neutralization of SARS‐CoV‐2, NL63, HKU1, and 229E in 38 COVID‐19 patients and 62 healthcare workers, and a further 182 samples to specifically study the relationship between SARS‐CoV‐2 and NL63. We found that although HCoV neutralization was very common there was little evidence that these antibodies neutralized SARS‐CoV‐2. Despite no evidence in cross‐neutralization, levels of NL63 neutralizing antibodies become elevated after exposure to SARS‐CoV‐2 through infection or following vaccination.

NL63 binds to the angiotensin-converting enzyme 2 (ACE2) receptor. 6 The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been rapidly transmitted and spread globally, causing over 200 million cases and more than 4 million deaths as of September 2021. Previous studies have demonstrated pre-existing immune responses to SARS-CoV-2 in people not exposed to the virus. This has been reported both for antibodies, [7][8][9] and T cell responses. 10,11 Though still under debate 12,13 this pre-existing immunity to a novel virus has largely been attributed to the four widely circulating HCoVs.
There has been great interest in the potential role of common cold coronaviruses in modulating the severity of COVID-19 disease. This is partly due to the fact that NL63 also uses the same ACE2 as its cellular receptor; therefore, it was questioned whether antibodies raised against NL63 would also bind to SARS-CoV-2. A recent report investigated whether seasonal HCoVs could protect against SARS-CoV-2 infection. 14 Anderson et al. used antibody-binding assays (ELISAs) to quantify antibodies against the HCoVs. However, these binding assays do not discriminate between neutralizing and non-neutralizing antibodies.
Pseudotype viruses (PVs) can be used to quantify neutralizing antibodies and have been shown to correlate with live-virus neutralization. [15][16][17] Herein, we used PVs bearing the Spike protein of SARS-CoV-2 and the seasonal HCoVs: NL63, HKU1, and 229E to investigate the relationships between common cold coronavirus immune responses and COVID-19 severity in healthcare workers and COVID-19 patients. We found that HCoV neutralization did not correlate with SARS-CoV-2 neutralization. This builds on previous work showing that HCoV binding does not protect against COVID-19 14 ; however, we also show that HCoV binding and HCoV neutralization are not strongly correlated. This finding highlights the importance of functional antibody assays, in addition to binding, when characterizing antibody responses. Despite a lack of cross-neutralization, we found that NL63 neutralization is boosted by SARS-CoV-2 vaccination and elevated after moderate to mild COVID-19 disease.

| Subject recruitment and plasma collection
Healthcare workers (HCWs) and COVID-19 patients were recruited from Royal Papworth Hospital, Cambridge, UK in the spring 2020. HCW were recruited through staff email over the course of 2 months (April 20, 2020-June 10, 2020) as part of a prospective study to establish seroprevalence and immune correlates of protective immunity to SARS-CoV-2. Following informed consent, staff were invited to complete a questionnaire to clarify whether they had swab polymerase chain reaction confirmed SARS-CoV-2 infection (routine swabbing was not available at that time and there was limited access to swabbing when symptomatic) and whether they had experienced symptoms that they felt may have been consistent with COVID-19 since January 2020.
Symptom severity was classified according to WHO severity classification into asymptomatic, mild, moderate, and severe disease. 18 The study was approved by Research Ethics Committee Wales, IRAS 96194 12/ WA/0148. Amendment 5. All participants provided written and informed consent before being enrolled in this study.
Plasma was taken from HCWs and convalescent COVID-19 patients 3-5 months after recruitment by collecting venous blood in lithium heparin tubes (S-Monovette) and centrifuged at 2300G.
Without disturbing the buffy coat, the plasma was transferred into 1.5 ml cryovials and heat-inactivated at 56°C for 30 min, aliquoted and stored at −80°C before use. We analyzed SARS-CoV-2 neutralization and HCoV neutralization for 38 COVID-19 patients, 23 seropositive HCWs, and 39 seronegative HCWs. These samples are described in more detail in Castillo-Olivares et al. 19 We had a particular interest in NL63 because along with SARS-CoV2 it uses the ACE2 receptor to enter cells. Because of our interest in NL63, we analyzed a further set of samples for SARS-CoV-2 and NL63 neutralization: 35 seropositive HCWs, 140 seronegative HCWs, and 7 COVID-19 patients (six were seropositive). We also collected follow-up samples from 21 of our HCWs 1 month after they received their first SARS-CoV-2 vaccination dose, approximately 9-12 months after they were first recruited to our study.

| Classifying sample serostatus
Samples' serostatus was determined according to SARS-CoV-2 IgG binding status. This was determined by neutralization (SARS-CoV-2 pMN IC50), and/or IgG binding to SARS-CoV-2 Spike, Nucleocapsid, and Spike receptor-binding domain (RBD) by a UKAS-accredited Luminex assay as described in. 19 These two methods of classification showed good agreement but as neither assay was performed on all samples a positive result on either assay classified the sample as seropositive. The seropositive cutoff for pMN was the 95% upper confidence interval of pre-pandemic samples in previous work. 19 The classification based on IgG binding is described in Baxendale et al., 20 but in brief, a linear support vector machine was trained to distinguish a set of pre-pandemic sera from COVID-19 patient sera. This classification method considers the three antigens jointly so there is no single cut-off to report. were maintained in Ham's F-12 medium supplemented with 10% FBS and 1% P/S. All cells were incubated at 37°C and 5% CO 2 . Cells were routinely passaged three times a week to prevent overconfluency.

| Pseudotype virus generation
PV generation was carried out as previously described. 21  PVs were HEK293T cells pre-transfected with ACE-2 and TMPRSS-2, 24 and CHO cells were used as target cells for HKU1 PVs. Plates were incubated at 37°C and 5% CO 2 for 48 h before lysis using Bright-Glo and assaying luciferase reporter gene activity in relative light units (RLU) using a luminometer. PV titers were reported in RLU/ml.

| HCoV neutralization and COVID-19 severity
We used multiple regression to compare HCoV neutralization titers between SARS-CoV-2 seropositive HCWs and COVID-19 patients after accounting for differences in age and sex. Age and sex effects were reported after dropping nonsignificant HCW/patient terms. A linear model predicting HCoV neutralization was fit separately for NL63, HKU1, and 229E. All statistical analyses were performed using R. 25 We fit a linear regression to predict COVID-19 severity in 81 seropositive people. This model included the natural log of the SARS-CoV-2 pMN IC50 and a binary term indicating whether or not the sample came from a hospitalized COVID-19 patient. We used an F ratio test to determine if the natural log of the NL63 pMN IC50 significantly improved the model fit. Plots of residuals, leverage, and qq-plots were used to assess the assumptions of the model.
The WHO COVID-19 severity score is ordinal so we also analyzed our data using a proportional odds logistic regression designed for ordinal variables to ensure our conclusions are robust to nonlinearity in the data. Similar to the linear regression, our proportional odds logistic regression predicted COVID-19 severity in 81 seropositive people using the natural log of SARS-CoV-2 pMN IC50 and whether or not the sample came from a COVID-19 patient as predictors. We used a likelihood ratio test to test if the natural log of NL63 pMN IC50 significantly predicted COVID-19 severity after accounting for the other variables. The assumption of proportional odds was assessed by visualizing coefficients of logistic regression models predicting severity equal to, or greater than i for i equals 2-7.

| Does SARS-CoV-2 exposure increase HCoV neutralization?
If SARS-CoV-2 infection increased HCoV antibody titer we would expect HCoV neutralization to be higher in seropositive samples. We compared HCoV neutralization between serostatus groups to test if SARS-CoV-2 infection increased HCoV neutralization. We used a linear model using serostatus, sex, and age as predictors.
To investigate the effect of vaccination against SARS-CoV-2 on NL63 neutralization we quantified NL63 neutralization of 21 HCW before and after receiving their first dose of the SARS-CoV-2 vaccination. The significance of any change before and after vaccination was calculated using a paired Wilcoxon signed-rank test. F I G U R E 2 HCoV neutralization by demographic. Neutralizing IC 50 value for HCoVs against sex (A) and age (B). Black horizontal lines represent geometric means, colored lines in are simple linear regression lines. Sample sizes: NL63 n = 84, HKU1 n = 43, 229E n = 47. We found a significant difference in HKU1 neutralization between sex (p = 0.004) and age (p = 0.039). We did not see any statistical significance in HCoVs NL63 nor 229E between sexes. We observed a significant difference in 229E neutralization and age (p = 0.037). *p < 0.05, **p < 0.01 ***p < 0.001. HCoVs, human coronaviruses

| Correlations between neutralization of different viruses and spike binding
We investigated the correlation between neutralization of SARS-CoV-2 and HCoVs using Spearman's rank. We also visualized all correlations between HCoV and SARS-CoV-2 neutralization and spike binding using a Spearman's rank correlation plot. 26 3 | RESULTS
NL63 neutralization, the only HCoV to use the same ACE2 receptor for cell entry, 27
Although SARS-CoV-2 spike binding was closely correlated with SARS-CoV-2 neutralization, there was little correlation between spike binding and neutralization for HCoVs. However, there was a strong positive correlation between spike binding to the different HCoVs ( Figure 8).

| DISCUSSION
HCoVs cause frequent mild infections in humans with most people becoming infected during infancy, 28 and reinfections remain a common occurrence after approximately 12 months. 29 Because of the frequency of HCoV infections much of the population will possess some immune response to one or more of the HCoVs. Therefore, it is important to identify any impact HCoV immune responses have on SARS-CoV-2, or F I G U R E 5 Comparing neutralization of SARS-CoV-2 seropositive and seronegative samples with the HCoVs (A-C). Our results revealed significant differences for NL63 (A) (n = 255) (p = 0.018) and SARS-CoV-2 (D) (n = 255) (p = <0.001). However, we observed no significance for HKU1 (B) (n = 75) (p = 0.143) and 229E (C) (n = 86) (p = 0.978). Horizontal black lines indicate geometric means. Samples were grouped as seropositive or seronegative regardless of being from patients or HCWs. Serostatus was based on IgG binding by Luminex assay or by SARS-CoV-2 pMN assay where a cut-off reciprocal IC 50 value was derived using pre-pandemic sera as described in the methods or in Castillo-Olivares et al. 19  Spike protein binding was highly correlated between the HCoVs; however, there was relatively little correlation between HCoVs and SARS-CoV-2. If the correlation between HCoV binding was driven by cross-reactive antibodies we would also expect them to correlate with SARS-CoV-2 as it is more closely related to the betacoronavirus HCoVs than 229E and NL63 are. We interpret the lack of correlation in spike binding between SARS-CoV-2 as evidence that HCoV spike antibodies are likely not cross-reactive but co-occurring, that is, people who are exposed to one of the HCoVs are likely to be exposed to other HCoVs. 29 We found that SARS-CoV-2 neutralization does not correlate with neutralization of any HCoV we tested. At first, this seems to contradict several studies reporting cross-reactive binding and neutralization 7,8 ; however, these studies found only a very small proportion of people not exposed to SARS-CoV-2 displayed crossreactive antibodies. Ng et al. 7 found that less than 1% of prepandemic samples showed SARS-CoV-2 RBD binding antibodies.
This suggests that the majority of HCoV antibodies do not crossreact with SARS-CoV-2 and is in keeping with our results that HCoV neutralization is not correlated with SARS-CoV-2 neutralization, nor does it provide protection against COVID-19, which is consistent with a similar study. 31 On the other hand, a study observed that a recent HCoV infection may provide some degree of protection. 32 We do not have information on timing of HCoV infection so cannot test this relationship in this study.
One of the limitations of pseudotype viruses is that they possess only the spike protein; therefore, the effects of other viral proteins remain in question. The nucleocapsid protein (N) shows highly conserved motifs in the N-terminus, observed across a wide range of the HCoVs and SARS-CoV-2. 33 Cross-reactivity between SARS-CoV-1 N-antibodies and several animal coronaviruses were previously described, despite lack of cross-reactive spike antibodies. 34 Similarly, several reports have found cross-reactive antibodies between HCoVs and SARS-CoV-2 S, M, and N proteins. 7-9 Importantly, a report observed that N-antibodies of several viruses activate the TRIM21 pathway, which then drives cytotoxic T-cell activation. 35 This highlights the multifaceted defence mechanisms against SARS-CoV-2 and the HCoVs, of which further studies in each of these areas may contribute to the debate regarding cross-protection between SARS-CoV-2 and HCoVs.

AUTHOR CONTRIBUTIONS
The study was conceptualized by David A. Wells, Diego Cantoni, Javier Castillo-Olivares, Nigel Temperton, and Jonathan L. Heeney. Hospital patient enrollments and sample collections were coordinated by Gianmarco Raddi and Helen Baxendale. Processing of blood samples was done by Angalee Nadesalingam, Andrew Chan, and Peter Smith.
Neutralization assays were carried out by Diego Cantoni, Martin Mayora-Neto, Cecilia Di Genova, George Carnell, and Alexander Sampson FACS assay was carried out by Matteo Ferrari. Data processing and statistical analyses were done by David A. Wells. All authors provided critical feedback on project direction, data analysis, and manuscript drafts. All authors had access to the data that was generated in this study. All authors approved the submitted version.